{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Table of Contents](./table_of_contents.ipynb)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ensemble Kalman Filters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from __future__ import division, print_function\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <style>\n",
       "        .output_wrapper, .output {\n",
       "            height:auto !important;\n",
       "            max-height:100000px; \n",
       "        }\n",
       "        .output_scroll {\n",
       "            box-shadow:none !important;\n",
       "            webkit-box-shadow:none !important;\n",
       "        }\n",
       "        </style>\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#format the book\n",
    "import book_format\n",
    "book_format.set_style()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> I am not well versed with Ensemble filters. I have implemented one for this book, and made it work, but I have not used one in real life. Different sources use slightly different forms of these equations. If I implement the equations given in the sources the filter does not work. It is possible that I am doing something wrong. However, in various places on the web I have seen comments by people stating that they do the kinds of things I have done in my filter to make it work. In short, I do not understand this topic well, but choose to present my lack of knowledge rather than to gloss over the subject. I hope to master this topic in the future and to author a more definitive chapter. At the end of the chapter I document my current confusion and questions. In any case if I got confused by the sources perhaps you also will, so documenting my confusion can help you avoid the same.\n",
    "\n",
    "\n",
    "The ensemble Kalman filter (EnKF) is very similar to the unscented Kalman filter (UKF) of the last chapter. If you recall, the UKF uses a set of deterministically chosen weighted sigma points passed through nonlinear state and measurement functions. After the sigma points are passed through the function, we find the mean and covariance of the points and use this as the filter's new mean and covariance. It is only an approximation of the true value, and thus suboptimal, but in practice the filter is highly accurate. It has the advantage of often producing more accurate estimates than the EKF does, and also does not require you to analytically derive the linearization of the state and measurement equations. \n",
    "\n",
    "The ensemble Kalman filter works in a similar way, except it uses a *Monte Carlo* method to choose a large numbers of sigma points. It came about from the geophysical sciences as an answer for the very large states and systems needed to model things such as the ocean and atmosphere. There is an interesting article on it's development in weather modeling in *SIAM News* [1]. The filter starts by randomly generating a large number of points distributed about the filter's initial state. This distribution is proportional to the filter's covariance $\\mathbf{P}$. In other words 68% of the points will be within one standard deviation of the mean, 95% percent within two standard deviations, and so on. Let's look at this in two dimensions. We will use `numpy.random.multivariate_normal()` function to randomly create points from a multivariate normal distribution drawn from the mean (5, 3) with the covariance\n",
    "\n",
    "$$\\begin{bmatrix}\n",
    "32 & 15 \\\\ 15 & 40\n",
    "\\end{bmatrix}$$\n",
    "\n",
    "I've drawn the covariance ellipse representing two standard deviations to illustrate how the points are distributed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 900x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from numpy.random import multivariate_normal\n",
    "from filterpy.stats import (covariance_ellipse, \n",
    "                            plot_covariance_ellipse)\n",
    "\n",
    "mean = (5, 3)\n",
    "P = np.array([[32, 15],\n",
    "              [15., 40.]])\n",
    "\n",
    "x,y = multivariate_normal(mean=mean, cov=P, size=2500).T\n",
    "plt.scatter(x, y, alpha=0.3, marker='.')\n",
    "plt.axis('equal')\n",
    "\n",
    "plot_covariance_ellipse(mean=mean, cov=P,\n",
    "                        variance=2.**2,\n",
    "                        facecolor='none')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The Algorithm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As I already stated, when the filter is initialized a large number of sigma points are drawn from the initial state ($\\mathbf{x}$) and covariance ($\\mathbf{P}$). From there the algorithm proceeds very similarly to the UKF. During the prediction step the sigma points are passed through the state transition function, and then perturbed by adding a bit of noise to account for the process noise. During the update step the sigma points are translated into measurement space by passing them through the measurement function, they are perturbed by a small amount to account for the measurement noise. The Kalman gain is computed from the \n",
    "\n",
    "We already mentioned the main difference between the UKF and EnKF - the UKF choses the sigma points deterministically. There is another difference, implied by the algorithm above. With the UKF we generate new sigma points during each predict step, and after passing the points through the nonlinear function we reconstitute them into a mean and covariance by using the *unscented transform*. The EnKF keeps propagating the originally created sigma points; we only need to compute a mean and covariance as outputs for the filter! \n",
    "\n",
    "Let's look at the equations for the filter. As usual, I will leave out the typical subscripts and superscripts; I am expressing an algorithm, not mathematical functions. Here $N$ is the number of sigma points, $\\chi$ is the set of sigma points."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Initialize Step"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$\\boldsymbol\\chi \\sim \\mathcal{N}(\\mathbf{x}_0, \\mathbf{P}_0)$$\n",
    "\n",
    "This says to select the sigma points from the filter's initial mean and covariance. In code this might look like\n",
    "\n",
    "```python\n",
    "N = 1000\n",
    "sigmas = multivariate_normal(mean=x, cov=P, size=N)\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Predict Step"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$\n",
    "\\begin{aligned}\n",
    "\\boldsymbol\\chi &= f(\\boldsymbol\\chi, \\mathbf{u}) + v_Q \\\\\n",
    "\\mathbf{x} &= \\frac{1}{N} \\sum_1^N \\boldsymbol\\chi\n",
    "\\end{aligned}\n",
    "$$\n",
    "\n",
    "That is short and sweet, but perhaps not entirely clear. The first line passes all of the sigma points through a use supplied state transition function and then adds some noise distributed according to the $\\mathbf{Q}$ matrix. In Python we might write\n",
    "\n",
    "```python\n",
    "for i, s in enumerate(sigmas):\n",
    "    sigmas[i] = fx(x=s, dt=0.1, u=0.)\n",
    "\n",
    "sigmas += multivariate_normal(x, Q, N)\n",
    "```\n",
    "\n",
    "The second line computes the mean from the sigmas. In Python we will take advantage of `numpy.mean` to do this very concisely and quickly.\n",
    "\n",
    "```python\n",
    "x = np.mean(sigmas, axis=0)\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now optionally compute the covariance of the mean. The algorithm does not need to compute this value, but it is often useful for analysis. The equation is\n",
    "\n",
    "$$\\mathbf{P} = \\frac{1}{N-1}\\sum_1^N[\\boldsymbol\\chi-\\mathbf{x}^-][\\boldsymbol\\chi-\\mathbf{x}^-]^\\mathsf{T}$$\n",
    "\n",
    "$\\boldsymbol\\chi-\\mathbf{x}^-$ is a one dimensional vector, so we will use `numpy.outer` to compute the $[\\boldsymbol\\chi-\\mathbf{x}^-][\\boldsymbol\\chi-\\mathbf{x}^-]^\\mathsf{T}$ term. In Python we might write\n",
    "\n",
    "```python\n",
    "    P = 0\n",
    "    for s in sigmas:\n",
    "        P += outer(s-x, s-x)\n",
    "    P = P / (N-1)\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Update Step"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the update step we pass the sigma points through the measurement function, compute the mean and covariance of the sigma points, compute the Kalman gain from the covariance, and then update the Kalman state by scaling the residual by the Kalman gain. The equations are\n",
    "\n",
    "$$\n",
    "\\begin{aligned}\n",
    "\\boldsymbol\\chi_h &= h(\\boldsymbol\\chi, u)\\\\\n",
    "\\mathbf{z}_{mean} &= \\frac{1}{N}\\sum_1^N \\boldsymbol\\chi_h \\\\ \\\\\n",
    "\\mathbf{P}_{zz} &= \\frac{1}{N-1}\\sum_1^N [\\boldsymbol\\chi_h - \\mathbf{z}_{mean}][\\boldsymbol\\chi_h - \\mathbf{z}_{mean}]^\\mathsf{T} + \\mathbf{R} \\\\\n",
    "\\mathbf{P}_{xz} &= \\frac{1}{N-1}\\sum_1^N [\\boldsymbol\\chi - \\mathbf{x}^-][\\boldsymbol\\chi_h - \\mathbf{z}_{mean}]^\\mathsf{T} \\\\\n",
    "\\\\\n",
    "\\mathbf{K} &= \\mathbf{P}_{xz} \\mathbf{P}_{zz}^{-1}\\\\ \n",
    "\\boldsymbol\\chi & = \\boldsymbol\\chi + \\mathbf{K}[\\mathbf{z} -\\boldsymbol\\chi_h + \\mathbf{v}_R] \\\\ \\\\\n",
    "\\mathbf{x} &= \\frac{1}{N} \\sum_1^N \\boldsymbol\\chi \\\\\n",
    "\\mathbf{P} &= \\mathbf{P} - \\mathbf{KP}_{zz}\\mathbf{K}^\\mathsf{T}\n",
    "\\end{aligned}\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is very similar to the linear KF and the UKF. Let's just go line by line.\n",
    "\n",
    "The first line,\n",
    "\n",
    "$$\\boldsymbol\\chi_h = h(\\boldsymbol\\chi, u),$$\n",
    "\n",
    "just passes the sigma points through the measurement function $h$. We name the resulting points $\\chi_h$ to distinguish them from the sigma points. In Python we could write this as\n",
    "\n",
    "```python\n",
    "sigmas_h = h(sigmas, u)\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The next line computes the mean of the measurement sigmas.\n",
    "\n",
    "$$\\mathbf{z}_{mean} = \\frac{1}{N}\\sum_1^N \\boldsymbol\\chi_h$$\n",
    "\n",
    "In Python we write\n",
    "\n",
    "```python\n",
    "z_mean = np.mean(sigmas_h, axis=0)\n",
    "```\n",
    "    \n",
    "Now that we have the mean of the measurement sigmas we can compute the covariance for every measurement sigma point, and the *cross variance* for the measurement sigma points vs the sigma points. That is expressed by these two equations\n",
    "\n",
    "$$\n",
    "\\begin{aligned}\n",
    "\\mathbf{P}_{zz} &= \\frac{1}{N-1}\\sum_1^N [\\boldsymbol\\chi_h - \\mathbf{z}_{mean}][\\boldsymbol\\chi_h - \\mathbf{z}_{mean}]^\\mathsf{T} + \\mathbf{R} \\\\\n",
    "\\mathbf{P}_{xz} &= \\frac{1}{N-1}\\sum_1^N [\\boldsymbol\\chi - \\mathbf{x}^-][\\boldsymbol\\chi_h - \\mathbf{z}_{mean}]^\\mathsf{T}\n",
    "\\end{aligned}$$\n",
    "\n",
    "We can express this in Python with\n",
    "\n",
    "```python\n",
    "P_zz = 0\n",
    "for sigma in sigmas_h:\n",
    "    s = sigma - z_mean\n",
    "    P_zz += outer(s, s)\n",
    "P_zz = P_zz / (N-1) + R\n",
    "\n",
    "P_xz = 0\n",
    "for i in range(N):\n",
    "    P_xz += outer(self.sigmas[i] - self.x, sigmas_h[i] - z_mean)\n",
    "P_xz /= N-1\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Computation of the Kalman gain is straightforward $\\mathbf{K} = \\mathbf{P}_{xz} \\mathbf{P}_{zz}^{-1}$.\n",
    "\n",
    "In Python this is\n",
    "\n",
    "```python\n",
    "K = np.dot(P_xz, inv(P_zz))```\n",
    "\n",
    "Next, we update the sigma points with\n",
    "\n",
    "$$\\boldsymbol\\chi  = \\boldsymbol\\chi + \\mathbf{K}[\\mathbf{z} -\\boldsymbol\\chi_h + \\mathbf{v}_R]$$ \n",
    "\n",
    "Here $\\mathbf{v}_R$ is the perturbation that we add to the sigmas. In Python we can implement this with\n",
    "\n",
    "```python\n",
    "v_r = multivariate_normal([0]*dim_z, R, N)\n",
    "for i in range(N):\n",
    "    sigmas[i] += dot(K, z + v_r[i] - sigmas_h[i])\n",
    "```\n",
    "\n",
    "\n",
    "Our final step is recompute the filter's mean and covariance.\n",
    "\n",
    "```python\n",
    "    x = np.mean(sigmas, axis=0)\n",
    "    P = self.P - dot(K, P_zz).dot(K.T)\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Implementation and Example"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I have implemented an EnKF in the `FilterPy` library. It is in many ways a toy. Filtering with a large number of sigma points gives us very slow performance. Furthermore, there are many minor variations on the algorithm in the literature. I wrote this mostly because I was interested in learning a bit about the filter. I have not used it for a real world problem, and I can give no advice on using the filter for the large problems for which it is suited. Therefore I will refine my comments to implementing a very simple filter. I will use it to track an object in one dimension, and compare the output to a linear Kalman filter. This is a filter we have designed many times already in this book, so I will design it with little comment. Our state vector will be\n",
    "\n",
    "$$\\mathbf{x} = \\begin{bmatrix}x\\\\ \\dot{x}\\end{bmatrix}$$\n",
    "\n",
    "The state transition function is\n",
    "\n",
    "$$\\mathbf{F} = \\begin{bmatrix}1&1\\\\0&1\\end{bmatrix}$$\n",
    "\n",
    "and the measurement function is\n",
    "\n",
    "$$\\mathbf{H} = \\begin{bmatrix}1&0\\end{bmatrix}$$\n",
    "\n",
    "The EnKF is designed for nonlinear problems, so instead of using matrices to implement the state transition and measurement functions you will need to supply Python functions. For this problem they can be written as:\n",
    "\n",
    "```python\n",
    "def hx(x):\n",
    "    return np.array([x[0]])\n",
    "\n",
    "def fx(x, dt):\n",
    "    return np.dot(F, x)\n",
    "```\n",
    "\n",
    "One final thing: the EnKF code, like the UKF code, uses a single dimension for $\\mathbf{x}$, not a two dimensional column matrix as used by the linear kalman filter code.\n",
    "\n",
    "Without further ado, here is the code."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 900x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import kf_book.book_plots as bp\n",
    "from numpy.random import randn\n",
    "from filterpy.kalman import EnsembleKalmanFilter as EnKF\n",
    "from filterpy.kalman import KalmanFilter\n",
    "from filterpy.common import Q_discrete_white_noise\n",
    "\n",
    "np.random.seed(1234)\n",
    "\n",
    "def hx(x):\n",
    "    return np.array([x[0]])\n",
    "\n",
    "def fx(x, dt):\n",
    "    return np.dot(F, x)\n",
    "    \n",
    "F = np.array([[1., 1.],[0., 1.]])\n",
    "\n",
    "x = np.array([0., 1.])\n",
    "P = np.eye(2) * 100.\n",
    "enf = EnKF(x=x, P=P, dim_z=1, dt=1., N=20, hx=hx, fx=fx)\n",
    "\n",
    "std_noise = 10.\n",
    "enf.R *= std_noise**2\n",
    "enf.Q = Q_discrete_white_noise(2, 1., .001)\n",
    "\n",
    "kf = KalmanFilter(dim_x=2, dim_z=1)\n",
    "kf.x = np.array([x]).T\n",
    "kf.F = F.copy()\n",
    "kf.P = P.copy()\n",
    "kf.R = enf.R.copy()\n",
    "kf.Q = enf.Q.copy()\n",
    "kf.H = np.array([[1., 0.]])\n",
    "\n",
    "measurements = []\n",
    "results = []\n",
    "ps = []\n",
    "kf_results = []\n",
    "\n",
    "zs = []\n",
    "for t in range (0,100):\n",
    "    # create measurement = t plus white noise\n",
    "    z = t + randn()*std_noise\n",
    "    zs.append(z)\n",
    "\n",
    "    enf.predict()\n",
    "    enf.update(np.asarray([z]))\n",
    "    \n",
    "    kf.predict()\n",
    "    kf.update(np.asarray([[z]]))\n",
    "\n",
    "    # save data\n",
    "    results.append (enf.x[0])\n",
    "    kf_results.append (kf.x[0,0])\n",
    "    measurements.append(z)\n",
    "    ps.append(3*(enf.P[0,0]**.5))\n",
    "\n",
    "results = np.asarray(results)\n",
    "ps = np.asarray(ps)\n",
    "\n",
    "plt.plot(results, label='EnKF')\n",
    "plt.plot(kf_results, label='KF', c='b', lw=2)\n",
    "bp.plot_measurements(measurements)\n",
    "plt.plot (results - ps, c='k',linestyle=':', lw=1, label='1$\\sigma$')\n",
    "plt.plot(results + ps, c='k', linestyle=':', lw=1)\n",
    "plt.fill_between(range(100), results - ps, results + ps, facecolor='y', alpha=.3)\n",
    "plt.legend(loc='best');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It can be a bit difficult to see, but the KF and EnKF start off slightly different, but soon converge to producing nearly the same values. The EnKF is a suboptimal filter, so it will not produce the optimal solution that the KF produces. However, I deliberately chose $N$ to be quite small (20) to guarantee that the EnKF output is quite suboptimal. If I chose a more reasonable number such as 2000 you would be unable to see the difference between the two filter outputs on this graph."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Outstanding Questions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All of this should be considered as *my* questions, not lingering questions in the literature. However, I am copying equations directly from well known sources in the field, and they do not address the discrepancies.\n",
    "\n",
    "First, in Brown [2] we have all sums multiplied by $\\frac{1}{N}$, as in \n",
    "\n",
    "$$ \\hat{x} = \\frac{1}{N}\\sum_{i=1}^N\\chi_k^{(i)}$$\n",
    "\n",
    "The same equation in Crassidis [3] reads (I'll use the same notation as in Brown, although Crassidis' is different)\n",
    "\n",
    "$$ \\hat{x} = \\frac{1}{N-1}\\sum_{i=1}^N\\chi_k^{(i)}$$\n",
    "\n",
    "The same is true in both sources for the sums in the computation for the covariances. Crassidis, in the context of talking about the filter's covariance, states that $N-1$ is used to ensure an unbiased estimate. Given the following standard equations for the mean and standard deviation (p.2 of Crassidis), this makes sense for the covariance.\n",
    "\n",
    "$$\n",
    "\\begin{aligned}\n",
    "\\mu &= \\frac{1}{N}\\sum_{i=1}^N[\\tilde{z}(t_i) - \\hat{z}(t_i)] \\\\\n",
    " \\sigma^2 &= \\frac{1}{N-1}\\sum_{i=1}^N\\{[\\tilde{z}(t_i) - \\hat{z}(t_i)] - \\mu\\}^2\n",
    "\\end{aligned}\n",
    "$$\n",
    "\n",
    "However, I see no justification or reason to use $N-1$ to compute the mean. If I use $N-1$ in the filter for the mean the filter does not converge and the state essentially follows the measurements without any filtering. However, I do see a reason to use it for the covariance as in Crassidis, in contrast to Brown. Again, I support my decision empirically - $N-1$ works in the implementation of the filter, $N$ does not.\n",
    "\n",
    "My second question relates to the use of the $\\mathbf{R}$ matrix. In Brown $\\mathbf{R}$ is added to $\\mathbf{P}_{zz}$ whereas it isn't in Crassidis and other sources. I have read on the web notes by other implementers that adding R helps the filter, and it certainly seems reasonable and necessary to me, so this is what I do. \n",
    "\n",
    "My third question relates to the computation of the covariance $\\mathbf{P}$. Again, we have different equations in Crassidis and Brown. I have chosen the implementation given in Brown as it seems to give me the  behavior that I expect (convergence of $\\mathbf{P}$ over time) and it closely compares to the form in the linear KF. In contrast I find the equations in Crassidis do not seem to converge much.\n",
    "\n",
    "My fourth question relates to the state estimate update. In Brown we have \n",
    "\n",
    "$$\\boldsymbol\\chi  = \\boldsymbol\\chi + \\mathbf{K}[\\mathbf{z} -\\mathbf{z}_{mean} + \\mathbf{v}_R]$$ \n",
    "\n",
    "whereas in Crassidis we have\n",
    "\n",
    "$$\\boldsymbol\\chi  = \\boldsymbol\\chi + \\mathbf{K}[\\mathbf{z} -\\boldsymbol\\chi_h + \\mathbf{v}_R]$$ \n",
    "\n",
    "To me the Crassidis equation seems logical, and it produces a filter that performs like the linear KF for linear problems, so that is the formulation that I have chosen. \n",
    "\n",
    "I am not comfortable saying either book is wrong; it is quite possible that I missed some point that makes each set of equations work. I can say that when I implemented them as written I did not get a filter that worked. I define \"work\" as performs essentially the same as the linear KF for linear problems. Between reading implementation notes on the web and reasoning about various issues I have chosen the implementation in this chapter, which does in fact seem to work correctly. I have yet to explore the significant amount of original literature that will likely definitively explain the discrepancies. I would like to leave this here in some form even if I do find an explanation that reconciles the various differences, as if I got confused by these books than probably others will as well."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## References"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- [1] Mackenzie, Dana. *Ensemble Kalman Filters Bring Weather Models Up to Date* Siam News,  Volume 36, Number 8, October 2003. http://www.siam.org/pdf/news/362.pdf\n",
    "\n",
    "- [2]  Brown, Robert Grover, and Patrick Y.C. Hwang. *Introduction to Random Signals and Applied Kalman Filtering, With MATLAB® excercises and solutions.* Wiley, 2012.\n",
    "\n",
    "- [3] Crassidis, John L., and John L. Junkins. *Optimal estimation of dynamic systems*. CRC press, 2011."
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
